Sonic Xplorer: A Machine Learning Approach for Parametric Exploration of Sound

Abstract

This paper presents Sonic Xplorer an interfaces that uses timbre adjectives for multiparametric control sound synthesis. The interface utilises an artificial neural network to create a personalised interface. Users can manipulate a large number of sound synthesis parameters without the need to learn or use the synthesiser’s complex interface by utilising programed sounds by expert users. Sonic Xplorer learns a correlation based on users’ ratings between timbre adjectives and the acoustic descriptors. Timbre adjectives are then used to describe the acoustic qualities of the desired sound. This paper discusses in detail the approach that has been followed to develop the system and the mapping and strategies users employed when using the interface in order to discover new sounds.

Affiliations

Centre for Interaction Design

Edinburgh Napier University

10 Colinton Road, EH10 5DT

Scotland

Contributors

Augoustinos Tsiros

Conference

Publication date: July 2017

Publication date (Print): July 2017

Pages: 144-149

ID: 10.14236/ewic/EVA2017.34

Copyright statement: © Tsiros. Published by BCS Learning and Development Ltd. Proceedings of EVA London 2017, UK

License:

This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Conference name: Electronic Visualisation and the Arts (EVA 2017)

Conference acronym: EVA

Conference number:

Conference location: London, UK

Conference date: 11 – 13 July 2017

Conference sponsor: Electronic Workshops in Computing (eWiC)

Conference theme: Electronic Visualisation and the Arts

Product

Product Information: 1477-9358 BCS Learning & Development

Self URI (article page): https://www.scienceopen.com/hosted-document?doi=10.14236/ewic/EVA2017.34

Self URI (journal page): https://ewic.bcs.org/

Categories

Series title: Electronic Workshops in Computing